C-Types Foreign Function Interface (`numpy.ctypeslib`)#

numpy.ctypeslib.as_array(obj, shape=None)[source]#

Create a numpy array from a ctypes array or POINTER.

The numpy array shares the memory with the ctypes object.

The shape parameter must be given if converting from a ctypes POINTER. The shape parameter is ignored if converting from a ctypes array

numpy.ctypeslib.as_ctypes(obj)[source]#: Create and return a ctypes object from a numpy array. Actually anything that exposes the __array_interface__ is accepted.

numpy.ctypeslib.as_ctypes_type(dtype)[source]#

Convert a dtype into a ctypes type.

Parameters:

dtypedtype: The dtype to convert

Returns:

ctype: A ctype scalar, union, array, or struct

Raises:

NotImplementedError: If the conversion is not possible

Notes

This function does not losslessly round-trip in either direction.

np.dtype(as_ctypes_type(dt)) will:

insert padding fields

reorder fields to be sorted by offset

discard field titles

as_ctypes_type(np.dtype(ctype)) will:

discard the class names of ctypes.Structures and ctypes.Unions

convert single-element ctypes.Unions into single-element ctypes.Structures

insert padding fields

numpy.ctypeslib.load_library(libname, loader_path)[source]#

It is possible to load a library using

>>> lib = ctypes.cdll[<full_path_name>] 

But there are cross-platform considerations, such as library file extensions, plus the fact Windows will just load the first library it finds with that name. NumPy supplies the load_library function as a convenience.

Changed in version 1.20.0: Allow libname and loader_path to take any path-like object.

Parameters:

libnamepath-like: Name of the library, which can have ‘lib’ as a prefix, but without an extension.
loader_pathpath-like: Where the library can be found.

Returns:

ctypes.cdll[libpath]library object: A ctypes library object

Raises:

OSError: If there is no library with the expected extension, or the library is defective and cannot be loaded.

numpy.ctypeslib.ndpointer(dtype=None, ndim=None, shape=None, flags=None)[source]#

Array-checking restype/argtypes.

An ndpointer instance is used to describe an ndarray in restypes and argtypes specifications. This approach is more flexible than using, for example, POINTER(c_double), since several restrictions can be specified, which are verified upon calling the ctypes function. These include data type, number of dimensions, shape and flags. If a given array does not satisfy the specified restrictions, a TypeError is raised.

Parameters:

dtypedata-type, optional

Array data-type.

ndimint, optional

Number of array dimensions.

shapetuple of ints, optional

Array shape.

flagsstr or tuple of str

Array flags; may be one or more of:

C_CONTIGUOUS / C / CONTIGUOUS

F_CONTIGUOUS / F / FORTRAN

OWNDATA / O

WRITEABLE / W

ALIGNED / A

WRITEBACKIFCOPY / X

Returns:

klassndpointer type object: A type object, which is an _ndtpr instance containing dtype, ndim, shape and flags information.

Raises:

TypeError: If a given array does not satisfy the specified restrictions.

Examples

>>> clib.somefunc.argtypes = [np.ctypeslib.ndpointer(dtype=np.float64,
...                                                  ndim=1,
...                                                  flags='C_CONTIGUOUS')]
... 
>>> clib.somefunc(np.array([1, 2, 3], dtype=np.float64))
... 

class numpy.ctypeslib.c_intp#

A ctypes signed integer type of the same size as numpy.intp.

Depending on the platform, it can be an alias for either c_int, c_long or c_longlong.

C-Types Foreign Function Interface (numpy.ctypeslib)#

C-Types Foreign Function Interface (`numpy.ctypeslib`)#